A number of digital imaging devices are in common use today for capturing images and videos including digital still cameras and digital video cameras. Such digital imaging devices are equipped with an image sensor designed to convert light received via lenses into electrical signals. A charge coupled device (CCD) sensor and a complementary metal oxide semiconductor (CMOS) sensor are a few examples of the image sensors included in an imaging device. The image sensor includes multiple photodiodes provided corresponding to multiple pixels constituting an image and arranged to output pixel data representing pixel values of the corresponding pixels. Further, Images/Videos are generally captured in RGB format. In capturing device, there are sensor arrays containing sensors for each pixel position. As the data is captured in RGB format, but the visible light contains other colours also, so filters are provided before these sensors to filter out unwanted colour frequencies. Now to capture R, G, B value at each pixel, one would require 3 filters as well as 3 sensors for each pixel. This is not an economic way to capture any image, so some colour filter arrays are employed to capture the image with 3 times less sensors. The colour filters filter the light by wavelength range, such that the separate filtered intensities include information about the specific colour of light. For example, the Bayer filter is used for separating the R,G,B color information in a way that each quartet of sensors contains two diagonally opposite sensors for Green, one for Red and one for Blue. The raw image data captured by the image sensor is then converted to a full-colour image (with intensities of all three primary colours represented at each pixel) by a demosaicing algorithm which is tailored for each type of colour filter.
The photodiodes in a digital imaging device may be aligned as an array of M×N pixels, with the incident illumination at location (i,j) denoted by I(i,j). Each photodiode location produces an output Y(i,j), which depends on the incident illumination I(i,j) according to a linear function Y(i,j)=m I(i,j)+b; where m is the sensitivity and b is the bias or offset of the defect. In an ideal scenario, the sensitivity m is unity and the bias b is zero, such that the output always directly reflects the input. Due to malfunction of the sensor the sensitivity may either be greater than 1 or may be 0 and the bias may be very high thus resulting in a pixel value higher or lower than the incident illumination. Malfunction of any of the photodiodes can result in certain discrepancy in the original pixel values, thus in an output image a higher or lower pixel value may be displayed. Depending upon the values of non-unity sensitivity and a non-zero bias, defective pixels can be of various types namely Dead Pixels, Stuck Pixels and Hot Pixels. Dead Pixels are produced by sensors with zero sensitivity and low or zero bias. Stuck Pixels are produced by sensors with zero sensitivity and high bias. Hot Pixels are produced by sensors with non-unity sensitivity and/or non-zero bias.
A number of techniques have been proposed for detecting any defective pixel caused by malfunction of image sensors among multiple pixels constituting a taken image or video frame. A prior art techniques disclose for example, detecting a white defect as a defective pixel in a single plate color video camera. The prior art techniques check the absence of a high-frequency component in a target pixel set as a processing object based on the frequency characteristics of peripheral pixels with different color filters from the color filter set on the target pixel, and identify the target pixel as a defective pixel in response to subsequent detection of the presence of the high frequency component in the target pixel. However, the existing techniques are either ineffective or inefficient in detecting defective pixels in a video sequence that comprises of a plurality of frames.
The erroneous sensors produce a pixel value that looks pretty much different from its actual value and the pixel values in the surrounding region. Although these erroneous sensors are detected at the time of manufacturing of capturing device and some error-correction mechanism is also provided for the same, still these problems in sensors can also occur in field working. The systems and methods described in the present disclosure deal with detection of such type of defective pixels present in a video in an efficient manner.